Determination　of　the　optimal　model　parameters　for　biochemical　systems　is　a　time　consuming　iterative　process.　In　this　study,　a　novel　hybrid　differential　evolution　(DE)　algorithm　based　on　the　differential　evolution　technique　and　a　local　search　strategy　is　developed　for　solving　kinetic　parameter　estimation　problems.　By　combining　the　merits　of　DE　with　Gauss-Newton　method,　the　proposed　hybrid　approach　employs　a　DE　algorithm　for　identifying　promising　regions　of　the　solution　space　followed　by　use　of　Gauss-Newton　method　to　determine　the　optimum　in　the　identified　regions.　Some　well-known　benchmark　estimation　problems　are　utilized　to　test　the　efficiency　and　the　robustness　of　the　proposed　algorithm　compared　to　other　methods　in　literature.　The　comparison　indicates　that　the　present　hybrid　algorithm　outperforms　other　estimation　techniques　in　terms　of　the　global　searching　ability　and　the　convergence　speed.　Additionally,　the　estimation　of　kinetic　model　parameters　for　a　feed　batch　fermentor　is　carried　out　to　test　the　applicability　of　the　proposed　algorithm.　The　result　suggests　that　the　method　can　be　used　to　estimate　suitable　values　of　model　parameters　for　a　complex　mathematical　model.